


"""simple docstring"""



import os

import jsonlines
import numpy as np
from tqdm import tqdm


lowercase_							=							20_48
lowercase_							=							40_96
lowercase_							=							42
lowercase_							=							os.environ.pop('PROCESS_TRAIN', 'false')
lowercase_							=							{'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def      UpperCAmelCase     (       _lowercase :   Any	)						-> List[str]:


	"""simple docstring"""




	def choose_first(_lowercase :   str			,   _lowercase :   List[Any]=False	):
		assert isinstance(_lowercase			,   _lowercase	)
		if len(_lowercase	) == 1:
			lowerCAmelCase_      							=			answer[0]
			return {k: [answer[k]] for k in answer} if is_long_answer else answer
		for a in answer:
			if is_long_answer:
				lowerCAmelCase_      							=			{k: [a[k]] for k in a}
			if len(a['''start_token''']	) > 0:
				break
		return a

	lowerCAmelCase_      							=			{'''id''': example['''id''']}
	lowerCAmelCase_      							=			example['''annotations''']
	lowerCAmelCase_      							=			annotation['''yes_no_answer''']
	if 0 in yes_no_answer or 1 in yes_no_answer:
		lowerCAmelCase_      							=			['''yes'''] if 1 in yes_no_answer else ['''no''']
		lowerCAmelCase_      							=			lowerCAmelCase_      							=			[]
		lowerCAmelCase_      							=			lowerCAmelCase_      							=			[]
		lowerCAmelCase_      							=			['''<cls>''']
	else:
		lowerCAmelCase_      							=			['''short''']
		lowerCAmelCase_      							=			choose_first(annotation['''short_answers''']	)
		if len(out['''start_token''']	) == 0:
			# answer will be long if short is not available
			lowerCAmelCase_      							=			['''long''']
			lowerCAmelCase_      							=			choose_first(annotation['''long_answer''']			,   is_long_answer=_lowercase	)
			lowerCAmelCase_      							=			[]
		answer.update(_lowercase	)

	# disregard some samples
	if len(answer['''start_token''']	) > 1 or answer["start_token"] == answer["end_token"]:
		lowerCAmelCase_      							=			True
	else:
		lowerCAmelCase_      							=			False

	lowerCAmelCase_      							=			['''start_token''', '''end_token''', '''start_byte''', '''end_byte''', '''text''']
	if not all(isinstance(answer[k]			,   _lowercase	) for k in cols	):
		raise ValueError('''Issue in ID'''			,   example['''id''']	)

	return answer
def      UpperCAmelCase     (       _lowercase :   Tuple			,   _lowercase :   Union[str, Any]=False	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			_get_single_answer(_lowercase	)
	# bytes are of no use
	del answer["start_byte"]
	del answer["end_byte"]

	# handle yes_no answers explicitly
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		lowerCAmelCase_      							=			example['''document''']['''tokens''']
		lowerCAmelCase_      							=			[]
		for i in range(len(doc['''token''']	)	):
			if not doc["is_html"][i]:
				context.append(doc['''token'''][i]	)
		return {
		    "context": " ".join(_lowercase	),
		    "answer": {
		        "start_token": -1_0_0,  # ignore index in cross-entropy
		        "end_token": -1_0_0,  # ignore index in cross-entropy
		        "category": answer["category"],
		        "span": answer["category"],  # extra
		    },
		}

	# later, help in removing all no answers
	if answer["start_token"] == [-1]:
		return {
		    "context": "None",
		    "answer": {
		        "start_token": -1,
		        "end_token": -1,
		        "category": "null",
		        "span": "None",  # extra
		    },
		}

	# handling normal samples

	lowerCAmelCase_      							=			['''start_token''', '''end_token''']
	answer.update({k: answer[k][0] if len(answer[k]	) > 0 else answer[k] for k in cols}	)  # e.g. [10] == 10

	lowerCAmelCase_      							=			example['''document''']['''tokens''']
	lowerCAmelCase_      							=			answer['''start_token''']
	lowerCAmelCase_      							=			answer['''end_token''']

	lowerCAmelCase_      							=			[]
	for i in range(len(doc['''token''']	)	):
		if not doc["is_html"][i]:
			context.append(doc['''token'''][i]	)
		else:
			if answer["start_token"] > i:
				start_token -= 1
			if answer["end_token"] > i:
				end_token -= 1
	lowerCAmelCase_      							=			''' '''.join(context[start_token:end_token]	)

	# checking above code
	if assertion:
		lowerCAmelCase_      							=			doc['''is_html'''][answer['''start_token'''] : answer['''end_token''']]
		lowerCAmelCase_      							=			doc['''token'''][answer['''start_token'''] : answer['''end_token''']]
		lowerCAmelCase_      							=			''' '''.join([old[i] for i in range(len(_lowercase	)	) if not is_html[i]]	)
		if new != old:
			print('''ID:'''			,   example['''id''']	)
			print('''New:'''			,   _lowercase			,   end='''\n'''	)
			print('''Old:'''			,   _lowercase			,   end='''\n\n'''	)

	return {
	    "context": " ".join(_lowercase	),
	    "answer": {
	        "start_token": start_token,
	        "end_token": end_token - 1,  # this makes it inclusive
	        "category": answer["category"],  # either long or short
	        "span": new,  # extra
	    },
	}
def      UpperCAmelCase     (       _lowercase :   List[Any]			,   _lowercase :   Optional[Any]			,   _lowercase :   List[str]=2_0_4_8			,   _lowercase :   str=4_0_9_6			,   _lowercase :   List[Any]=True	)						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			get_context_and_ans(_lowercase			,   assertion=_lowercase	)
	lowerCAmelCase_      							=			out['''answer''']

	# later, removing these samples
	if answer["start_token"] == -1:
		return {
		    "example_id": example["id"],
		    "input_ids": [[-1]],
		    "labels": {
		        "start_token": [-1],
		        "end_token": [-1],
		        "category": ["null"],
		    },
		}

	lowerCAmelCase_      							=			tokenizer(example['''question''']['''text''']			,   out['''context''']	).input_ids
	lowerCAmelCase_      							=			input_ids.index(tokenizer.sep_token_id	) + 1

	# return yes/no
	if answer["category"][0] in ["yes", "no"]:  # category is list with one element
		lowerCAmelCase_      							=			[]
		lowerCAmelCase_      							=			[]
		lowerCAmelCase_      							=			input_ids[:q_len]
		lowerCAmelCase_      							=			range(_lowercase			,   len(_lowercase	)			,   max_length - doc_stride	)
		for i in doc_start_indices:
			lowerCAmelCase_      							=			i + max_length - q_len
			lowerCAmelCase_      							=			input_ids[i:end_index]
			inputs.append(q_indices + slice	)
			category.append(answer['''category'''][0]	)
			if slice[-1] == tokenizer.sep_token_id:
				break

		return {
		    "example_id": example["id"],
		    "input_ids": inputs,
		    "labels": {
		        "start_token": [-1_0_0] * len(_lowercase	),
		        "end_token": [-1_0_0] * len(_lowercase	),
		        "category": category,
		    },
		}

	lowerCAmelCase_      							=			out['''context'''].split()
	lowerCAmelCase_      							=			splitted_context[answer['''end_token''']]
	lowerCAmelCase_      							=			len(
	    tokenizer(
	        ''' '''.join(splitted_context[: answer['''start_token''']]	)			,   add_special_tokens=_lowercase			,   ).input_ids	)
	lowerCAmelCase_      							=			len(
	    tokenizer(''' '''.join(splitted_context[: answer['''end_token''']]	)			,   add_special_tokens=_lowercase	).input_ids	)

	answer["start_token"] += q_len
	answer["end_token"] += q_len

	# fixing end token
	lowerCAmelCase_      							=			len(tokenizer(_lowercase			,   add_special_tokens=_lowercase	).input_ids	)
	if num_sub_tokens > 1:
		answer["end_token"] += num_sub_tokens - 1

	lowerCAmelCase_      							=			input_ids[answer['''start_token'''] : answer['''end_token'''] + 1]  # right & left are inclusive
	lowerCAmelCase_      							=			answer['''start_token''']
	lowerCAmelCase_      							=			answer['''end_token''']

	if assertion:
		lowerCAmelCase_      							=			tokenizer.decode(_lowercase	)
		if answer["span"] != new:
			print('''ISSUE IN TOKENIZATION'''	)
			print('''OLD:'''			,   answer['''span''']	)
			print('''NEW:'''			,   _lowercase			,   end='''\n\n'''	)

	if len(_lowercase	) <= max_length:
		return {
		    "example_id": example["id"],
		    "input_ids": [input_ids],
		    "labels": {
		        "start_token": [answer["start_token"]],
		        "end_token": [answer["end_token"]],
		        "category": answer["category"],
		    },
		}

	lowerCAmelCase_      							=			input_ids[:q_len]
	lowerCAmelCase_      							=			range(_lowercase			,   len(_lowercase	)			,   max_length - doc_stride	)

	lowerCAmelCase_      							=			[]
	lowerCAmelCase_      							=			[]
	lowerCAmelCase_      							=			[]
	lowerCAmelCase_      							=			[]  # null, yes, no, long, short
	for i in doc_start_indices:
		lowerCAmelCase_      							=			i + max_length - q_len
		lowerCAmelCase_      							=			input_ids[i:end_index]
		inputs.append(q_indices + slice	)
		assert len(inputs[-1]	) <= max_length, "Issue in truncating length"

		if start_token >= i and end_token <= end_index - 1:
			lowerCAmelCase_      							=			start_token - i + q_len
			lowerCAmelCase_      							=			end_token - i + q_len
			answers_category.append(answer['''category'''][0]	)  # ["short"] -> "short"
		else:
			lowerCAmelCase_      							=			-1_0_0
			lowerCAmelCase_      							=			-1_0_0
			answers_category.append('''null'''	)
		lowerCAmelCase_      							=			inputs[-1][start_token : end_token + 1]

		answers_start_token.append(_lowercase	)
		answers_end_token.append(_lowercase	)
		if assertion:
			if new != old and new != [tokenizer.cls_token_id]:
				print('''ISSUE in strided for ID:'''			,   example['''id''']	)
				print('''New:'''			,   tokenizer.decode(_lowercase	)	)
				print('''Old:'''			,   tokenizer.decode(_lowercase	)			,   end='''\n\n'''	)
		if slice[-1] == tokenizer.sep_token_id:
			break

	return {
	    "example_id": example["id"],
	    "input_ids": inputs,
	    "labels": {
	        "start_token": answers_start_token,
	        "end_token": answers_end_token,
	        "category": answers_category,
	    },
	}
def      UpperCAmelCase     (       _lowercase :   Tuple			,   _lowercase :   str			,   _lowercase :   Tuple=2_0_4_8			,   _lowercase :   Any=4_0_9_6			,   _lowercase :   Union[str, Any]=False	)						-> Any:


	"""simple docstring"""




	lowerCAmelCase_      							=			get_strided_contexts_and_ans(
	    _lowercase			,   _lowercase			,   doc_stride=_lowercase			,   max_length=_lowercase			,   assertion=_lowercase			,   )

	return example
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   Union[str, Any]	)						-> Dict:


	"""simple docstring"""




	with jsonlines.open(_lowercase			,   '''a'''	) as writer:
		for example in tqdm(_lowercase			,   total=len(_lowercase	)			,   desc='''Saving samples ... '''	):
			lowerCAmelCase_      							=			example['''labels''']
			for ids, start, end, cat in zip(
			    example['''input_ids''']			,   labels['''start_token''']			,   labels['''end_token''']			,   labels['''category''']			,   ):
				if start == -1 and end == -1:
					continue  # leave waste samples with no answer
				if cat == "null" and np.random.rand() < 0.6:
					continue  # removing 50 % samples
				writer.write(
				    {
				        '''input_ids''': ids,
				        '''start_token''': start,
				        '''end_token''': end,
				        '''category''': CATEGORY_MAPPING[cat],
				    }	)


if __name__ == "__main__":
		from datasets import load_dataset

		from transformers import BigBirdTokenizer

		lowercase_							=							load_dataset('natural_questions')
		lowercase_							=							BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

		lowercase_							=							data['train' if PROCESS_TRAIN == 'true' else 'validation']

		lowercase_							=							{
		    'tokenizer': tokenizer,
		    'doc_stride': DOC_STRIDE,
		    'max_length': MAX_LENGTH,
		    'assertion': False,
		}
		lowercase_							=							data.map(prepare_inputs, fn_kwargs=fn_kwargs)
		lowercase_							=							data.remove_columns(['annotations', 'document', 'id', 'question'])
		print(data)

		np.random.seed(SEED)
		lowercase_							=							'nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
		save_to_disk(data, file_name=cache_file_name)



"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)